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CDO Vision New York: Unlocking Generative AI’s ROI Potential

While challenges exist in budgeting, measuring ROI, and scaling implementation, a strategic approach can unlock substantial value across functions

The CDO Vision New York, organized by AIM Research, brought together a panel of distinguished industry experts to explore the transformative potential of Generative AI and strategies to quantify its return on investment (ROI) across business verticals. This insightful discussion, titled “Unlocking the Value of Generative AI: Strategies for Quantifying ROI in Business Investments,” shed light on real-world applications, implementation challenges, and innovative approaches to measuring the multidimensional impact of this disruptive technology.

Leaders from diverse industries shared their first-hand experiences and insights, highlighting the tangible benefits of Generative AI in accelerating research and development, streamlining content creation processes, and enhancing operational efficiencies. Case studies showcased remarkable outcomes, such as substantial time savings in scientific literature synthesis, increased productivity for sales teams, and improved regulatory compliance through AI-assisted submissions.

Exploring ROI Measurement with Generative AI Implementation

In the rapidly evolving landscape of artificial intelligence, Generative AI has emerged as a powerful tool for organizations seeking to drive innovation and streamline operations. A leading player in the animal health industry has recently shared insights into their successful implementation of Generative AI, showcasing its potential to deliver measurable returns on investment (ROI).

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The organization has strategically harnessed the capabilities of Generative AI across multiple facets of their business, including research and development (R&D), clinical development, regulatory filings, sales, and marketing. By identifying key priority areas, they have targeted specific use cases where Generative AI can create substantial value.

Within R&D, Generative AI has accelerated target identification, optimized early research efforts, and strengthened regulatory submissions. By anticipating and addressing potential questions from regulatory bodies, the company aims to expedite the regulatory pipeline, ultimately shortening time-to-market for their innovative solutions.

On the commercial front, Generative AI has streamlined content creation processes. By developing an AI model trained on past reviews and comments, they have implemented a system that pre-approves content submissions, reducing time and resources required for legal, medical, and regulatory reviews.

One notable success involves implementing a Generative AI model that listens in on sales calls. With customer consent, the AI assistant generates call summaries and recommends follow-up actions, automatically integrating into the company’s CRM system. This has led to a remarkable increase in the number of calls sales representatives can handle daily, directly impacting productivity and operational efficiency.

Furthermore, the company has introduced a “research scientist co-pilot,” a Generative AI assistant that serves as an intelligent aide for their research scientists. By synthesizing scientific literature, publications, and internal data, this co-pilot has demonstrated the ability to glean valuable insights in hours, a task that would have traditionally taken weeks through manual research efforts.

These real-world examples highlight the tangible impact of Generative AI on ROI measurement across various organizational functions. By accelerating innovation cycles, streamlining administrative tasks, and enhancing operational efficiency, Generative AI has proven its ability to drive measurable value creation.

As adoption continues to gain momentum, companies across industries are poised to unlock new avenues for growth, innovation, and competitive advantage by strategically leveraging this transformative technology.

Understanding ROI Factors and Framework for Generative AI Integration

As artificial intelligence (AI) transforms industries, organizations must adopt a comprehensive approach to measuring return on investment (ROI). Experts recommend evaluating AI use cases based on potential short-term, mid-term, and long-term gains across six key dimensions: positioning, risk management, operational excellence, financial performance, innovation, and trust building.

The “PROFIT” framework provides a structure to capture AI’s multifaceted impact, assessing brand elevation, risk mitigation, productivity enhancements, profitability improvements, disruptive innovations, and trust-building among stakeholders. Establishing relevant metrics and KPIs aligned with these dimensions before implementation is crucial.

However, realizing AI’s value hinges on several critical factors:

  • Clearly defining the use case to align resources and strategies effectively.
  • Ensuring data readiness and quality, as AI relies heavily on clean, relevant data inputs.
  • Distinguishing short-term and long-term ROI horizons, as some benefits may accrue over extended periods.
  • Evaluating revenue implications (internally, externally, and from new offerings) while considering costs like computational resources and skilled personnel.
  • Fostering cross-functional collaboration among data science, engineering, legal, and ethics teams.
  • Addressing ethical and legal implications proactively to build trust and mitigate risks.

Moreover, organizations should consider the opportunity cost of not implementing AI, as competitors may gain advantages through early adoption.

By adopting this holistic approach, encompassing temporal perspectives, multidimensional value assessment, and cross-functional synergies, organizations can unlock AI’s true potential and maximize returns while maintaining ethical integrity.

Framing the Investment Horizon of Generative AI: Short-term vs. Long-term Perspective

Industry experts emphasize the importance of building strong relationships with business finance teams, particularly the Chief Financial Officer (CFO), who can be a valuable ally in this endeavor.

One of the primary challenges lies in the scalability of Generative AI technologies. The transition from proof-of-concept (POC) to production can significantly increase costs across various categories, such as computational resources and energy consumption. For instance, Generating a single image can drain the battery of an iPhone, raising questions about the feasibility and cost-effectiveness of large-scale deployments.

However, experts draw parallels between the current state of Generative AI and the early days of disruptive technologies like Tesla’s electric vehicles. Just as the idea of a battery-powered car was initially met with skepticism, the potential of Generative AI to revolutionize industries may seem unconventional today. This presents a critical window of opportunity for organizations to position themselves as early adopters and gain a competitive advantage.

To navigate this landscape effectively, organizations are encouraged to adopt a portfolio approach to budgeting, incorporating optionality. By breaking down projects into smaller, incremental steps, organizations can achieve smaller wins, gain momentum, and mitigate the risk of allocating substantial budgets upfront. This approach becomes particularly valuable in periods of market fluctuations, where options and flexibility are paramount.

Moreover, experts emphasize the need to account for the opportunity cost of not embracing Generative AI. As disruptive technologies reshape industries, organizations that fail to adapt risk being left behind by competitors who successfully harness the power of these innovations. The second and third-order impacts of Generative AI on traditional financial modeling and ROI projections cannot be ignored, necessitating a reevaluation of established methodologies.

To effectively communicate the value proposition of Generative AI initiatives to organizations, leaders must be prepared to substantiate the potential alpha (excess return) that these projects can generate. This may require collaborative efforts, extensive analysis, and the development of new models that capture the multifaceted impact of Generative AI on business operations and revenue streams.

Additionally, it is crucial to approach Generative AI adoption with humility, acknowledging the inherent uncertainties and the rapidly evolving nature of the technology. Embracing volatility and maintaining flexibility in adapting to changing capabilities will be essential for sustaining the arc of business value over time.

By adopting a strategic and adaptable approach to budgeting, measuring ROI, and fostering cross-functional collaboration, organizations can navigate the challenges and capitalize on the opportunities presented by the Generative AI revolution.

Conclusion:

Embracing the Generative AI revolution demands a forward-thinking and agile mindset from organizations. While challenges exist in budgeting, measuring ROI, and scaling implementation, a strategic approach can unlock substantial value across functions

Cultivating strong finance partnerships, recognizing scalability challenges, adopting a portfolio budgeting approach with optionality, and framing ROI through a multidimensional lens are critical. Organizations must also consider the opportunity cost of not embracing Generative AI as disruptive technologies reshape industries.

A collaborative, cross-functional approach coupled with flexibility to adapt to rapidly evolving capabilities is paramount. Those that embrace this mindset will drive innovation, enhance efficiency, and gain a competitive edge in the Generative AI era.

Anshika Mathews
Anshika Mathews
Anshika is an Associate Research Analyst working for the AIM Leaders Council. She holds a keen interest in technology and related policy-making and its impact on society. She can be reached at anshika.mathews@aimresearch.co
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